ahmadsm1/

MiMo-Audio-7B-Instruct

Existing audio language models typically rely on task-specific fine-tuning to accomplish particular audio tasks. In contrast, humans are able to generalize to new audio tasks with only a few examples or simple instructions. GPT-3 has shown that scaling next-token prediction pretraining enables strong generalization capabilities in text, and we believe this paradigm is equally applicable to the audio domain. By scaling MiMo-Audio's pretraining data to over one hundred million of hours, we observe the emergence of few-shot learning capabilities across a diverse set of audio tasks. We develop a systematic evaluation of these capabilities and find that MiMo-Audio-7B-Base achieves SOTA performance on both speech intelligence and audio understanding benchmarks among open-source models. Beyond standard metrics, MiMo-Audio-7B-Base generalizes to tasks absent from its training data, such as voice conversion, style transfer, and speech editing. MiMo-Audio-7B-Base also demonstrates powerful speech continuation capabilities, capable of generating highly realistic talk shows, recitations, livestreaming and debates. At the post-training stage, we curate a diverse instruction-tuning corpus and introduce thinking mechanisms into both audio understanding and generation. MiMo-Audio-7B-Instruct achieves open-source SOTA on audio understanding benchmarks, spoken dialogue benchmarks and instruct-TTS evaluations, approaching or surpassing closed-source models.

MiMo-Audio-Tokenizer is a 1.2B-parameter Transformer operating at 25 Hz. It employs an eight-layer RVQ stack to generate 200 tokens per second. By jointly optimizing semantic and reconstruction objectives, we train MiMo-Audio-Tokenizer from scratch on a 10-million-hour corpus, achieving superior reconstruction quality and facilitating downstream language modeling.

MiMo-Audio couples a patch encoder, an LLM, and a patch decoder to improve modeling efficiency for high-rate sequences and bridge the length mismatch between speech and text. The patch encoder aggregates four consecutive time steps of RVQ tokens into a single patch, downsampling the sequence to a 6.25 Hz representation for the LLM. The patch decoder autoregressively generates the full 25 Hz RVQ token sequence via a delayed-generation scheme.

Spin up the MiMo-Audio demo in minutes with the built-in Gradio app.

This launches a local Gradio interface where you can try MiMo-Audio interactively.

Enter the local paths for MiMo-Audio-Tokenizer and MiMo-Audio-7B-Instruct, then enjoy the full functionality of MiMo-Audio!

We provide an example script to explore the in-context learning capabilities of MiMo-Audio-7B-Base.
See: inference_example_pretrain.py

To try the instruction-tuned model MiMo-Audio-7B-Instruct, use the corresponding inference script.
See: inference_example_sft.py

Full evaluation suite are available at 🌐MiMo-Audio-Eval.

This toolkit is designed to evaluate MiMo-Audio and other recent audio LLMs as mentioned in the paper. It provides a flexible and extensible framework, supporting a wide range of datasets, tasks, and models.

Please contact us at mimo@xiaomi.com or open an issue if you have any questions.